Extensive studies, including the Diabetes Control and Complications Trial (DCCT) (See DCCT Research Group: The Effect Of Intensive Treatment Of Diabetes On The Development And Progression Of Long-Term Complications Of Insulin-Dependent Diabetes Mellitus. New England Journal of Medicine, 329: 978–986, 1993), the Stockholm Diabetes Intervention Study (See Reichard P, Phil M: Mortality and Treatment Side Effects During Long-term Intensified Conventional Insulin Treatment in the Stockholm Diabetes Intervention Study. Diabetes, 43: 313–317, 1994), and the United Kingdom Prospective Diabetes Study (See UK Prospective Diabetes Study Group: Effect of Intensive Blood Glucose Control With Metformin On Complications In Patients With Type 2 Diabetes (UKPDS 34). Lancet, 352: 837–853, 1998), have repeatedly demonstrated that the most effective way to prevent the long term complications of diabetes is by strictly maintaining blood glucose (BG) levels within a normal range using intensive insulin therapy.
However, the same studies have also documented some adverse effects of intensive insulin therapy, the most acute of which is the increased risk of frequent severe hypoglycemia (SH), a condition defined as an episode of neuroglycopenia which precludes self-treatment and requires external help for recovery (See DCCT Research Group: Epidemiology of Severe Hypoglycemia In The Diabetes Control and Complications Trial. American Journal of Medicine, 90: 450–459, 1991, and DCCT Research Group: Hypoglycemia in the Diabetes Control and Complications Trial. Diabetes, 46: 271–286, 1997). Since SH can result in accidents, coma, and even death, patients and health care providers are discouraged from pursuing intensive therapy. Consequently, hypoglycemia has been identified as a major barrier to improved glycemic control (Cryer P E: Hypoglycemia is the Limiting Factor in the Management Of Diabetes. Diabetes Metab Res Rev, 15: 42–46, 1999).
Thus, patients with diabetes face a life-long optimization problem of maintaining strict glycemic control without increasing their risk of hypoglycemia. A major challenge related to this problem is the creation of simple and reliable methods that are capable of evaluating both patients' glycemic control and their risk of hypoglycemia, and that can be applied in their everyday environments.
It has been well known for more than twenty years that glycosylated hemoglobin is a marker for the glycemic control of individuals with Diabetes Mellitus (Type I or Type II). Numerous researchers have investigated this relationship and have found that glycosylated hemoglobin generally reflects the average BG levels of a patient over the previous two months. Since in the majority of patients with diabetes the BG levels fluctuate considerably over time, it was suggested that the real connection between integrated glucose control and HbA1c would be observed only in patients known to be in stable glucose control over a long period of time.
Early studies of such patients produced an almost deterministic relationship between the average BG level in the preceding 5 weeks and HbA1c, and this curvilinear association yielded a correlation coefficient of 0.98 (See Aaby Svendsen P, Lauritzen T, Soegard U, Nerup J (1982). Glycosylated Hemoglobin and Steady-State Mean Blood Glucose Concentration in Type 1 (Insulin-Dependent) Diabetes, Diabetologia, 23, 403–405). In 1993 the DCCT concluded that HbA1c was the “logical nominee” for a gold-standard glycosylated hemoglobin assay, and the DCCT established a linear relationship between the preceding mean BG and HbA1c (See Santiago J V (1993). Lessons from the Diabetes Control and Complications Trial, Diabetes, 42, 1549–1554).
Guidelines were developed indicating that an HbA1c of 7% corresponds to a mean BG of 8.3 mM (150 mg/dl), an HbA1c of 9% corresponds to a mean BG of 11.7 mM (210 mg/dl), and a 1% increase in HbA1c corresponds to an increase in mean BG of 1.7 mM (30 mg/dl, 2). The DCCT also suggested that because measuring the mean BG directly is not practical, one could assess a patient's glycemic control with a single, simple test, namely HbA1c. However, studies clearly demonstrate that HbA1c is not sensitive to hypoglycemia.
Indeed, there is no reliable predictor of a patient's immediate risk of SH from any data. The DCCT concluded that only about 8% of future SH could be predicted from known variables such as the history of SH, low HbA1c, and hypoglycemia unawareness. One recent review details the current clinical status of this problem, and provides options for preventing SH, that are available to patients and their health care providers (See Bolli, G B: How To Ameliorate The Problem of Hypoglycemia In Intensive As Well As Nonintensive Treatment Of Type I Diabetes. Diabetes Care, 22, Supplement 2: B43–B52, 1999).
Contemporary home BG monitors provide the means for frequent BG measurements through Self-Monitoring of BG (SMBG). However, the problem with SMBG is that there is a missing link between the data collected by the BG monitors, and HbA1c and hypoglycemia. In other words, there are currently no reliable methods for evaluating HbA1c and recognizing imminent hypoglycemia based on SMBG readings (See Bremer T and Gough D A: Is blood glucose predictable from previous values? A solicitation for data. Diabetes 48:445–451, 1999).
Thus, an object of this invention is to provide this missing link by proposing three distinct, but compatible, algorithms for evaluating HbA1c and the risk of hypoglycemia from SMBG data, to be used to predict the short-term and long-term risks of hypoglycemia, and the long-term risk of hyperglycemia.
The inventors have previously reported that one reason for a missing link between the routinely available SMBG data and the evaluation of HbA1c and the risk of hypoglycemia, is that the sophisticated methods of data collection and clinical assessment used in diabetes research, are infrequently supported by diabetes-specific and mathematically sophisticated statistical procedures.
Responding to the need for statistical analyses that take into account the specific distribution of BG data, the inventors developed a symmetrizing transformation of the blood glucose measurement scale (See Kovatchev BP, Cox DJ, Gonder-Frederick LA and WL Clarke (1997). Symmetization of the Blood Glucose Measurement Scale and Its Applications, Diabetes Care, 20, 1655–1658) that works as the follows. The BG levels are measured in mg/dl in the United States, and in mmol/L (or mM) in most other countries. The two scales are directly related by 18 mg/dl=1 mM. The entire BG range is given in most references as 1.1 to 33.3 mM, and this is considered to cover practically all observed values. According to the recommendations of the DCCT (See DCCT Research Group (1993) The Effect Of Intensive Treatment of Diabetes On the Development and Progression of Long-Term Complications of Insulin-Dependent Diabetes Mellitus. New England Journal of Medicine, 329, pp 978–986) the target BG range—also known as the euglycemic range—for a person with diabetes is 3.9 to 10 mM, hypoglycemia occurs when the BG falls below 3.9 mM, and hyperglycemia is when the BG rises above 10 mM. Unfortunately, this scale is numerically asymmetric—the hyperglycemic range (10 to 33.3 mM) is wider than the hypoglycemic range (1.1 to 3.9 mM), and the euglycemic range (3.9 to 10 mM) is not centered within the scale. The inventors correct this asymmetry by introducing a transformation, f(BG), which is a continuous function defined on the BG range [1.1, 33.3], having the two-parameter analytical form:f(BG, α, β)=[(ln(BG))α−β], α, β>0and which satisfies the assumptions:                A1: f(33.3, α, β)=−f(1.1, α, β) and        A2: f(10.0, α, β)=−f(3.9, α, β).        
Next, f(BG) is multiplied by a third scaling parameter to fix the minimum and maximum values of the transformed BG range at −√{square root over (10)} and √{square root over (10)} respectively. These values are convenient since a random variable with a standard normal distribution has 99.8% of its values within the interval [−√{square root over (10)}, √{square root over (10)}]. If BG is measured in mmol/l, when solved numerically with respect to the assumptions A1 and A2, the parameters of the function f(BG, α, β) are α=1.026, β=1.861, and the scaling parameter is y=1.794. If BG is measured in mg/dl instead, the parameters are computed to be α=1.084, β=5.381, and γ=1.509.
Thus, when BG is measured in mmol/l, the symmetrizing transformation is f(BG)=1.794[(ln(BG))1.026−1.861], and when BG is measured in mg/dl the symmetrizing transformation is f(BG)=1.509[(ln(BG))1.084−5.381].
On the basis of the symmetrizing transformation f(BG) the inventors introduced the Low BG Index—a new measure for assessing the risk of hypoglycemia from SMBG readings (See Cox D J, Kovatchev B P, Julian D M, Gonder-Frederick L A, Polonsky W H, Schlundt D G, Clarke W L: Frequency of Severe Hypoglycemia In IDDM Can Be Predicted From Self-Monitoring Blood Glucose Data. Journal of Clinical Endocrinology and Metabolism, 79: 1659–1662, 1994, and Kovatchev B P, Cox D J, Gonder-Frederick L A Young-Hyman D, Schlundt D, Clarke W L. Assessment of Risk for Severe Hypoglycemia Among Adults With IDDM: Validation of the Low Blood Glucose Index, Diabetes Care 21:1870–1875, 1998). Given a series of SMBG data the Low BG Index is computed as the average of 10f(BG)2 taken for values of f(BG)<0 and 0 otherwise. Also suggested was a High BG Index, computed in a symmetrical to the Low BG Index manner, however this index did not find its practical application.
Using the Low BG Index in a regression model the inventors were able to account for 40% of the variance of SH episodes in the subsequent 6 months based on the SH history and SMBG data, and later to enhance this prediction to 46% (See Kovatchev B P, Straume M, Farhi L S, Cox D J: Estimating the Speed of Blood Glucose Transitions and its Relationship With Severe Hypoglycemia. Diabetes, 48: Supplement 1, A363, 1999).
In addition, the inventors developed some data regarding HbA1c and SMBG (See Kovatchev B P, Cox D J, Straume M, Farhy L S. Association of Self-monitoring Blood Glucose Profiles with Glycosylated Hemoglobin. In: Methods in Enzymology, vol. 321: Numerical Computer Methods, Part C, Michael Johnson and Ludvig Brand, Eds., Academic Press, NY; 2000).
These developments became a part of the theoretical background of this invention. In order to bring this theory into practice, several key theoretical components, among other things, as described in the following sections, were added. In particular, three methods were developed for employing the evaluation of HbA1c, long-term and short-term risk for hypoglycemia. The development of these methods was, but not limited thereto, based on detailed analysis of data for 867 individuals with diabetes that included more than 300,000 SMBG readings, records of severe hypoglycemia and determinations of HbA1c.
The inventors have therefore sought to improve upon the aforementioned limitations associated with the conventional methods, and thereby provide simple and reliable methods that are capable of evaluating both patients' glycemic control and their risk of hypoglycemia, and that can be applied in their everyday environments.